When a surgery is performed on an organ, such as liver or lung, to resect an affected region, the following operation is required in the case of the liver, for example. Specifically, blood vessels, hepatic parenchyma, and a tumor region are extracted from an X-ray CT image of the liver, and based on the positions of core lines, diameters, and the like of the extracted blood vessels, a blood vessel that dominates the extracted tumor region is identified. In this manner, the blood vessel that supplies nutrition to the tumor is identified, and the region dominated by the identified blood vessel is determined appropriately as a region to be resected. In the surgery of resecting a region of the liver, this operation is necessary in order to appropriately resect a portion of a portal vein that supplies nutrition to the tumor and a region that is dominated by the portion of the portal vein and may be supplied with substances to be noted such as cancer cells, in such a manner as to maintain the function of the liver even after the resection. For this reason, it is important to perform a thorough simulation as to which region of the organ is to be resected before the surgery. Further, in order to perform this simulation, it is necessary to extract central paths of the blood vessels running in the lung and the liver accurately.
As an image recognition technology for extracting a linear structure such as bronchi from a three-dimensional medical image acquired by CT or the like, there has been proposed a method using a Hessian matrix as disclosed in Patent Literatures 1 and 2.
According to the method disclosed in Japanese Patent Application Publication No. 2010-220742, first, after a three-dimensional medical image is subjected to multi-resolution transformation, eigenvalues of a Hessian matrix are analyzed in an image of each resolution to extract linear structure elements. Each of those linear structure elements has such a feature that only one of three eigenvalues obtained by the eigenvalue analysis is close to 0. Next, the analysis results about the images of the respective resolutions are unified, to thereby extract the linear structure elements (blood vessels) in various sizes from the three-dimensional medical image. Then, those extracted linear structure elements are connected to each other through use of a minimum spanning tree algorithm or the like. As a result, data on a tree structure representing a tubular structure in the three-dimensional medical image is acquired. Note that, when the linear structure elements are connected to each other through use of the minimum spanning tree algorithm, a cost function based on a positional relationship between the linear structure elements and a principal axis direction of each of the linear structure elements, which is represented by an eigenvector corresponding to the eigenvalue close to 0, is used.
Further, according to the method disclosed in Japanese Patent Application Publication No. 2011-098195, a candidate region for a linear structure is extracted from a three-dimensional medical image, and from among candidate points included in the extracted candidate region for the linear structure, representative points are selected through use of graph matching so as to form a shape model that is most similar to a predetermined set shape. Then, for example, a graph structure generated based on the candidate points is corrected in such a manner as to match the graph structure with the shape model generated based on the representative points. In this manner, it is possible to generate the graph structure accurately.